In the current internet era, data security and privacy have become quintessential, therefore the various encryption mechanisms, which aid to secure the data have come to the forefront. However, these emerging techniques of encryption, in network applications, have created a challenge in managing and securing the network. To ease the burden of the end point security analysts in achieving transparency about the intent of the incoming traffic in a network, encrypted traffic classification has to be performed. Earlier, traditional traffic classification methods were used for traffic classification. Since the accuracy of the traditional methods dropped with the increase in traffic and wide spread application of encryption in networks, usage of Machine Learning and Deep Learning and other techniques evolved. Though the Machine Learning and Deep Learning models gave high accuracy in traffic identification, to identify non-encrypted traffic, encrypted traffic analysis is still a challenging task. Techniques like entropy estimation of the incoming packet payload have been applied in this paper, along with Machine Learning and Deep Learning algorithm for encrypted traffic classification. From the results, it can be ascertained that this methodology is able to help us identity the application that generated the encrypted packet with an accuracy of 94%.